Here, \(n\)  is the number of samples, \(\hat{y}_i\)  is the model prediction and \(y_i\)  is the true measurement. The training is done with MSE loss function for a maximum of 300 epochs, and early stopping based on validation loss with patience 20 is used. Batch size is set to 128, and Adam optimizer [1]} is employed. Both TENT and Transformer use custom learning rate schedule [2]} while 3d-CNN uses a learning rate adapted by Adam optimizer.
The optimal hyper-parameters for each model that were found empirically can be seen in Table REF .
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